Functional avidity of HIV-specific CD8 T-cell responses. by Selena Viganò (430641)

“…<p><b>A.</b> Representative examples of functional avidity of HIV-specific CD8 T-cell responses in patients with acute (PHI-B-09; circles), chronic progressive (CP-B-08; squares) or chronic non-progressive (LTNP-2082; triangles) infection after stimulation with decreasing concentrations of B*0801-_EIYKRWII peptides. …”

Larvicidal and macrofilaricidal efficacy of Closantel and Morantel against mosquito larvae and <i>Setaria digitata</i> nematodes by Megan Hall (506400)

Data_Sheet_1_The Clinical Trial Outcomes of Med-Zenith PT-Valve in the Treatment of Patients With Severe Pulmonary Regurgitation.docx by Xiaoke Shang (3589976)

Glucose and G-6-P levels modulate HK2-<i>SOX10</i> mRNA association and <i>SOX10</i> mRNA translation. by Ana Luisa Dian (22258504)

Table_1_Incidence trends and disparities in Helicobacter pylori related malignancy among US adults, 2000–2019.DOCX by Yafang Lai (14184500)

“…</p>Results<p>The incidence in non-cardia GC and gastric NHL significantly decreased during 2000–2019, while the rate plateaued for cardia GC (AAPCs, −1.0% [95% CI, −1.1%−0.9%], −2.6% [95% CI, −2.9%−2.3%], and −0.2% [95% CI, −0.7%−0.3%], respectively). …”

Image2_Using electronic admission data to monitor temporal trends in local medication use: Experience from an Australian tertiary teaching hospital.tif by Richard J. Woodman (8830580)

“…Of the 21 medication classes, 15 increased (p < 0.005), including antithrombotic agents; OR = 1.18 [1.16–1.21], proton pump inhibitors; OR = 1.14 [1.12–1.17], statins; OR = 1.12; [1.09–1.14], and renin-angiotensin system agents; OR = 1.06 [1.04–1.08], whilst 3 decreased (p < 0.005) including anti-inflammatory drugs (OR = 0.55; 99.5% CI = 0.53–0.58), cardiac glycosides (OR = 0.81; 99.5% CI = 0.78–0.86) and opioids (OR = 0.82; 99.5% CI = 0.79–0.83). …”

Image5_Using electronic admission data to monitor temporal trends in local medication use: Experience from an Australian tertiary teaching hospital.tif by Richard J. Woodman (8830580)

“…Of the 21 medication classes, 15 increased (p < 0.005), including antithrombotic agents; OR = 1.18 [1.16–1.21], proton pump inhibitors; OR = 1.14 [1.12–1.17], statins; OR = 1.12; [1.09–1.14], and renin-angiotensin system agents; OR = 1.06 [1.04–1.08], whilst 3 decreased (p < 0.005) including anti-inflammatory drugs (OR = 0.55; 99.5% CI = 0.53–0.58), cardiac glycosides (OR = 0.81; 99.5% CI = 0.78–0.86) and opioids (OR = 0.82; 99.5% CI = 0.79–0.83). …”

Image3_Using electronic admission data to monitor temporal trends in local medication use: Experience from an Australian tertiary teaching hospital.tif by Richard J. Woodman (8830580)

“…Of the 21 medication classes, 15 increased (p < 0.005), including antithrombotic agents; OR = 1.18 [1.16–1.21], proton pump inhibitors; OR = 1.14 [1.12–1.17], statins; OR = 1.12; [1.09–1.14], and renin-angiotensin system agents; OR = 1.06 [1.04–1.08], whilst 3 decreased (p < 0.005) including anti-inflammatory drugs (OR = 0.55; 99.5% CI = 0.53–0.58), cardiac glycosides (OR = 0.81; 99.5% CI = 0.78–0.86) and opioids (OR = 0.82; 99.5% CI = 0.79–0.83). …”

Image4_Using electronic admission data to monitor temporal trends in local medication use: Experience from an Australian tertiary teaching hospital.tif by Richard J. Woodman (8830580)

“…Of the 21 medication classes, 15 increased (p < 0.005), including antithrombotic agents; OR = 1.18 [1.16–1.21], proton pump inhibitors; OR = 1.14 [1.12–1.17], statins; OR = 1.12; [1.09–1.14], and renin-angiotensin system agents; OR = 1.06 [1.04–1.08], whilst 3 decreased (p < 0.005) including anti-inflammatory drugs (OR = 0.55; 99.5% CI = 0.53–0.58), cardiac glycosides (OR = 0.81; 99.5% CI = 0.78–0.86) and opioids (OR = 0.82; 99.5% CI = 0.79–0.83). …”

Image1_Using electronic admission data to monitor temporal trends in local medication use: Experience from an Australian tertiary teaching hospital.pdf by Richard J. Woodman (8830580)

“…Of the 21 medication classes, 15 increased (p < 0.005), including antithrombotic agents; OR = 1.18 [1.16–1.21], proton pump inhibitors; OR = 1.14 [1.12–1.17], statins; OR = 1.12; [1.09–1.14], and renin-angiotensin system agents; OR = 1.06 [1.04–1.08], whilst 3 decreased (p < 0.005) including anti-inflammatory drugs (OR = 0.55; 99.5% CI = 0.53–0.58), cardiac glycosides (OR = 0.81; 99.5% CI = 0.78–0.86) and opioids (OR = 0.82; 99.5% CI = 0.79–0.83). …”

Table1_Using electronic admission data to monitor temporal trends in local medication use: Experience from an Australian tertiary teaching hospital.docx by Richard J. Woodman (8830580)

“…Of the 21 medication classes, 15 increased (p < 0.005), including antithrombotic agents; OR = 1.18 [1.16–1.21], proton pump inhibitors; OR = 1.14 [1.12–1.17], statins; OR = 1.12; [1.09–1.14], and renin-angiotensin system agents; OR = 1.06 [1.04–1.08], whilst 3 decreased (p < 0.005) including anti-inflammatory drugs (OR = 0.55; 99.5% CI = 0.53–0.58), cardiac glycosides (OR = 0.81; 99.5% CI = 0.78–0.86) and opioids (OR = 0.82; 99.5% CI = 0.79–0.83). …”

Data_Sheet_1_Contractility of Induced Pluripotent Stem Cell-Cardiomyocytes With an MYH6 Head Domain Variant Associated With Hypoplastic Left Heart Syndrome.pdf by Min-Su Kim (183958)

“…Functional assessments performed between D20-D23 revealed that MYH6-R443P variant CMs contracted more slowly (40 ± 2 vs. 47 ± 2 contractions/min, P < 0.05), shortened less (5.6 ± 0.5 vs. 8.1 ± 0.7% of cell length, P < 0.05), and exhibited slower shortening rates (19.9 ± 1.7 vs. 28.1 ± 2.5 μm/s, P < 0.05) and relaxation rates (11.0 ± 0.9 vs. 19.7 ± 2.0 μm/s, P < 0.05). …”

Chemical characterization and evaluation of antimicrobial and cutaneous wound healing potentials of gold nanoparticles using Allium saralicum R.M. Fritsch by Hu, X.

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Data_Sheet_1_Global burden of maternal disorders attributable to malnutrition from 1990 to 2019 and predictions to 2035: worsening or improving?.docx by Tongtong Xu (4959505)

“…Both ASMR and ASDR showed a significant decreasing trend with increasing SDI. …”

Table1_Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening.XLSX by Yijun Wu (325401)

Table4_Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening.XLSX by Yijun Wu (325401)

Table2_Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening.XLSX by Yijun Wu (325401)

Table3_Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening.XLSX by Yijun Wu (325401)

Image1_Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening.JPEG by Yijun Wu (325401)

DataSheet1_Exploration of potential novel drug targets and biomarkers for small cell lung cancer by plasma proteome screening.PDF by Yijun Wu (325401)